Enhancing Privacy in Real-Time Video Streams: Techniques, Challenges, and Benchmark datasets Powered by Deep Learning
DOI:
https://doi.org/10.37385/jaets.v7i2.8200Keywords:
Privacy, De-identification, CNN, YOLO, DatasetsAbstract
The exponential growth of video surveillance, live streaming platforms, and AI-driven analytics has created unprecedented threats to visual privacy. Traditional de-identification methods (pixelation, blurring) fail to balance privacy protection with contextual utility in dynamic environments. This systematic review of 30+ peer-reviewed studies uses a taxonomical framework to classify machine learning-based privacy preservation techniques into three domains: intervention methods (sensor saturation, broadcasting commands), obfuscation strategies (encryption, morphing, adaptive blurring), and secure processing pipelines. We test convolutional neural networks (CNNs), YOLO-based object detection systems, and hybrid approaches including GAN-driven synthetic data substitution using public datasets (MARS, DukeMTMC, Market-1501). CNN-YOLO hybrid architectures provide 30+ FPS real-time performance with 92-98% detection accuracy, while GAN-based anonymization preserves visual usefulness better than traditional approaches. Dataset scalability, illumination variability handling (accuracy drops 15-23% in low-light settings), occlusion resilience, and adversarial attack vulnerability remain key shortcomings. Although promising, lightweight encryption approaches for edge devices cost 12-18% processing speed and lack defined privacy-utility trade-off measures. Implications: This work unifies computer vision, cryptography, and privacy engineering into a single taxonomy, showing that context-aware frameworks need multi-level security designs to manage varied threat scenarios. Our findings help practitioners choose strategies depending on deployment restrictions (computational resources, latency, privacy regulations), yet 67% of reviewed methods lack real-world validation outside controlled datasets.This review uniquely synthesizes intervention, obfuscation, and secure processing research to provide uniform standards, context-adaptive privacy frameworks, and adversarially-robust de-identification systems. Five key research directions—federated learning for distributed privacy, attention-mechanism-enhanced detection under occlusion, and explainable AI for privacy-utility optimization—will shape the next generation of ethical, scalable visual privacy solutions in pervasive video analytics.
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References
Ahmed, M., Wang, Y., Maher, A., & Bai, X. (2022). Fused RetinaNet for small target detection in aerial images. International Journal of Remote Sensing, 43(8), 2813–2836. https://doi.org/10.1080/01431161.2022.2071115
Ahn, B., & Jang, S.-W. (2021). Context-adaptive blocking for protecting personal information exposed to social multimedia content. Multimedia Tools and Applications, 80(26–27), 34249–34267. https://doi.org/10.1007/s11042-020-10042-0
Al-lahham, A., Zaheer, M. Z., Tastan, N., & Nandakumar, K. (2024). Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12416–12425. https://doi.org/10.1109/CVPR52733.2024.01180
Al-Obaidi, S., Al-Khafaji, H., & Abhayaratne, C. (2020). Modeling Temporal Visual Salience for Human Action Recognition Enabled Visual Anonymity Preservation. IEEE Access, 8, 213806–213824. https://doi.org/10.1109/ACCESS.2020.3039740
Al-Tamimi, M. and T. H. Y. 2024. (2024). An Exhaustive Survey of Deep Learning Techniques in ECG Signals. Ibn AL-Haitham Journal For Pure and Applied Sciences., 37(3), 428–441. https://doi.org/https://doi.org/10.30526/37.3.3901
Al-Tamimi, M. S. H., Amer, F., & Ali, M. (2023). Face mask detection based on algorithm YOLOv5s. Int. J. Nonlinear Anal. Appl, 14, 2008–6822. https://doi.org/10.22075/ijnaa.2022.28178.3824
Ancilotto, A., Paissan, F., & Farella, E. (2023). PhiNet-GAN: Bringing real-time face swapping to embedded devices. 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops), 677–682. https://doi.org/10.1109/PerComWorkshops56833.2023.10150292
Arkushin, D., Cohen, B., Peleg, S., & Fried, O. (2024). GEFF: Improving Any Clothes-Changing Person ReID Model Using Gallery Enrichment with Face Features. 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), 143–153. https://doi.org/10.1109/WACVW60836.2024.00021
Asres, M. W., Jiao, L., & Walter Omlin, C. (2026). Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performance. IEEE Transactions on Information Forensics and Security, 21, 1–16. https://doi.org/10.1109/TIFS.2025.3630347
Baoyuan, C., Yitong, L., & Kun, S. (2021). Research on Object Detection Method Based on FF-YOLO for Complex Scenes. IEEE Access, 9, 127950–127960. https://doi.org/10.1109/ACCESS.2021.3108398
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. https://doi.org/10.48550/arXiv.2004.10934
Brkić, K., Hrkać, T., & Kalafatić, Z. (2017). Protecting the privacy of humans in video sequences using a computer vision-based de-identification pipeline. Expert Systems with Applications, 87, 41–55. https://doi.org/10.1016/j.eswa.2017.05.067
Casas, E., Ramos, L., Bendek, E., & Rivas-Echeverría, F. (2023). Assessing the Effectiveness of YOLO Architectures for Smoke and Wildfire Detection. IEEE Access, 11, 96554–96583. https://doi.org/10.1109/ACCESS.2023.3312217
Chen, D., Chang, Y., Yan, R., & Yang, J. (2007). Tools for protecting the privacy of specific individuals in video. Eurasip Journal on Advances in Signal Processing, 2007. https://doi.org/10.1155/2007/75427
Cho, Y.-J., & Yoon, K.-J. (2017). PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification. https://doi.org/10.1109/TIP.2018.2815840
Çiftçi, S., Korshunov, P., Akyüz, A. O., & Ebrahimi, T. (2015). Using false colors to protect visual privacy of sensitive content (B. E. Rogowitz, T. N. Pappas, & H. de Ridder, Eds.; p. 93941L). https://doi.org/10.1117/12.2083189
Climent-Pérez, P., & Florez-Revuelta, F. (2021). Protection of visual privacy in videos acquired with RGB cameras for active and assisted living applications. Multimedia Tools and Applications, 80(15), 23649–23664. https://doi.org/10.1007/s11042-020-10249-1
Climent-Pérez, P., Spinsante, S., Mihailidis, A., & Florez-Revuelta, F. (2020). A review on video-based active and assisted living technologies for automated lifelogging. Expert Systems with Applications, 139, 112847. https://doi.org/10.1016/j.eswa.2019.112847
Deshmukh, S., Doshi, K., & Borse, Y. (2018). Securing Images Using Layered Morphing. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 1–6. https://doi.org/10.1109/ICCUBEA.2018.8697888
Dhayea, A. M., El Abbadi, N. K., & Hasan, Z. G. A. (2024). Human Skin Detection and Segmentation Based on Convolutional Neural Networks. Iraqi Journal of Science, 65(2), 1102–1116. https://doi.org/10.24996/ijs.2024.65.2.40
Dominguez-Dager, B., Escalona, F., Gomez-Donoso, F., & Cazorla, M. (2026). CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis. Scientific Data, 13(1), 109. https://doi.org/10.1038/s41597-025-06425-3
Dworak, D. (2020). BlurNet: Keeping Collected Data Private with a Neural Network Based Pipeline (pp. 1237–1248). https://doi.org/10.1007/978-3-030-50936-1_103
Erdelyi, A., Barat, T., Valet, P., Winkler, T., & Rinner, B. (2014). Adaptive cartooning for privacy protection in camera networks. 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 44–49. https://doi.org/10.1109/AVSS.2014.6918642
Erlina, T., & Fikri, M. (2023). A YOLO Algorithm-based Visitor Detection System for Small Retail Stores using Single Board Computer. Journal of Applied Engineering and Technological Science (JAETS), 4(2), 908–920. https://doi.org/10.37385/jaets.v4i2.1872
Fan, M., Chen, C., Wang, C., Zhou, W., & Huang, J. (2023). On the Robustness of Split Learning Against Adversarial Attacks. https://doi.org/10.3233/FAIA230330
Gevers, T., & Smeulders, A. (2016). Foreword. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): 9914 LNCS (p. V). Springer Verlag. https://doi.org/10.1007/978-3-319-46466-4
Girshick, R. (2015). Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV), 1440–1448. https://doi.org/10.1109/ICCV.2015.169
He, K., Zhang, X., Ren, S., & Sun, J. (2014). Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition (pp. 346–361). https://doi.org/10.1007/978-3-319-10578-9_23
Hellmann, F., Mertes, S., Benouis, M., Hustinx, A., André, E., Hsieh, C., De, T.-B., Hsieh, T.-C., Conati, C., & Krawitz, P. (2023). GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions ACM Reference Format (Vol. 1, Number 1). https://doi.org/doi:10.1145/3641107.
Huang, W., Li, G., Chen, Q., Ju, M., & Qu, J. (2021). CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote Sensing Target Detection. Remote Sensing, 13(5), 847. https://doi.org/10.3390/rs13050847
Huang, X., Chen, W., & Yang, W. (2021). Improved Algorithm Based on The Deep Integration of Googlenet and Residual Neural Network. Journal of Physics: Conference Series, 1757(1), 012069. https://doi.org/10.1088/1742-6596/1757/1/012069
Huangfu, Z., & Li, S. (2023). Lightweight You Only Look Once v8: An Upgraded You Only Look Once v8 Algorithm for Small Object Identification in Unmanned Aerial Vehicle Images. Applied Sciences, 13(22), 12369. https://doi.org/10.3390/app132212369
Hukkelas, H., & Lindseth, F. (2023). DeepPrivacy2: Towards Realistic Full-Body Anonymization. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 1329–1338. https://doi.org/10.1109/WACV56688.2023.00138
Hussain, M. (2023). YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection. Machines, 11(7), 677. https://doi.org/10.3390/machines11070677
Hussain, M. (2024). YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO. IEEE Access, 12, 42816–42833. https://doi.org/10.1109/ACCESS.2024.3378568
Jassim, A. H., Ali, N. H., Al-Taie, A., & Majed, D. M. (2025). Accelerating Face Mask Detection Training Model Based on Multi-GPUs and Multi-core CPU. Baghdad Science Journal, 22(6), 2099–2118. https://doi.org/10.21123/2411-7986.4979
Kapadia, A., Henderson, T., Fielding, J. J., & Kotz, D. (2007). Virtual walls: Protecting digital privacy in pervasive environments. In H.-W. Gellersen, R. Want, & A. Schmidt (Eds.), Pervasive Computing (pp. 162–179). Springer. https://doi.org/10.1007/978-3-540-72037-9_10
Karanam, S., Gou, M., Wu, Z., Rates-Borras, A., Camps, O., & Radke, R. J. (2019). A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(3), 523–536. https://doi.org/10.1109/TPAMI.2018.2807450
Kim, K.-J., Kim, P.-K., Chung, Y.-S., & Choi, D.-H. (2018). Performance Enhancement of YOLOv3 by Adding Prediction Layers with Spatial Pyramid Pooling for Vehicle Detection. 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 1–6. https://doi.org/10.1109/AVSS.2018.8639438
Korshunov, P., & Ebrahimi, T. (2013). Using face morphing to protect privacy. 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 208–213. https://doi.org/10.1109/AVSS.2013.6636641
Lee, H., Kim, M. U., Kim, Y., Lyu, H., & Yang, H. J. (2021). Development of a Privacy-Preserving UAV System With Deep Learning-Based Face Anonymization. IEEE Access, 9, 132652–132662. https://doi.org/10.1109/ACCESS.2021.3113186
Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., & Wei, X. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. https://doi.org/10.48550/arXiv.2209.02976
Li, R., & Yang, J. (2018). Improved YOLOv2 Object Detection Model. 2018 6th International Conference on Multimedia Computing and Systems (ICMCS), 1–6. https://doi.org/10.1109/ICMCS.2018.8525895
Li, Y., & Lyu, S. (2019). De-identification Without Losing Faces. Proceedings of the ACM Workshop on Information Hiding and Multimedia Security, 83–88. https://doi.org/10.1145/3335203.3335719
Li, Z., Lu, S., Dong, Y., & Guo, J. (2023). MSFFA: a multi-scale feature fusion and attention mechanism network for crowd counting. The Visual Computer, 39(3), 1045–1056. https://doi.org/10.1007/s00371-021-02383-0
Liu, J., Xia, Y., & Tang, Z. (2021). Privacy-preserving video fall detection using visual shielding information. The Visual Computer, 37(2), 359–370. https://doi.org/10.1007/s00371-020-01804-w
Liu, Y., Peng, B., Shi, P., Yan, H., Zhou, Y., Han, B., Zheng, Y., Lin, C., Jiang, J., Fan, Y., Gao, T., Wang, G., Liu, J., Lu, X., & Xie, D. (2019). iQIYI-VID: A Large Dataset for Multi-modal Person Identification. Proceedings of the 26th ACM International Conference on Multimedia, 1360–1363. https://doi.org/10.48550/arXiv.1811.07548
Luo, Z., Zou, Y., Yang, Y., Durante, Z., Huang, D.-A., Yu, Z., Xiao, C., Fei-Fei, L., & Anandkumar, A. (2023). Differentially Private Video Activity Recognition. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 20029–20038. https://doi.org/https://doi.org/10.48550/arXiv.2306.15742
M, Mr. N. (2024). Protecting Privacy in Surveillance Systems via Selective Video Encryption. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 08(05), 1–5. https://doi.org/10.55041/IJSREM34554
Maolood, A. T., Gbashi, E. K., & Mahmood, E. S. (2022). Novel lightweight video encryption method based on ChaCha20 stream cipher and hybrid chaotic map. International Journal of Electrical and Computer Engineering (IJECE), 12(5), 4988. https://doi.org/10.11591/ijece.v12i5.pp4988-5000
Maximov, M., Elezi, I., & Leal-Taixé, L. (2020). CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks. https://doi.org/10.1109/CVPR42600.2020.00549
Mirzazadeh, A., Dubost, F., Pike, M., Maniar, K., Zuo, M., Lee-Messer, C., & Rubin, D. (2023). ATCON: Attention Consistency for Vision Models. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 1880–1889. https://doi.org/10.1109/WACV56688.2023.00192
Mohammed, N. A., Abdulateef, O. F., Hamad, A. H., & Abdullah, O. I. (2024). Performance Analysis of Different Machine Learning Algorithms for Predictive Maintenance. Al-Khwarizmi Engineering Journal, 20(2), 26–38. https://doi.org/10.22153/kej.2024.11.003
Mulajkar, R., & Yede, S. (2024). YOLO Version v1 to v8 Comprehensive Review. 2024 International Conference on Inventive Computation Technologies (ICICT), 472–478. https://doi.org/10.1109/ICICT60155.2024.10544452
Petitcolas, F. A. P., Anderson, R. J., & Kuhn, M. G. (1999). Information hiding-a survey. Proceedings of the IEEE, 87(7), 1062–1078. https://doi.org/10.1109/5.771065
Raees, S., & Al-Tamimi, M. (2024). The Role of Artificial Intelligence in Providing People With Privacy: Survey. Journal of Applied Engineering and Technological Science (JAETS), 5(2), 813–829. https://doi.org/10.37385/jaets.v5i2.4013
Ragab, M. G., Abdulkadir, S. J., Muneer, A., Alqushaibi, A., Sumiea, E. H., Qureshi, R., Al-Selwi, S. M., & Alhussian, H. (2024). A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023). IEEE Access, 12, 57815–57836. https://doi.org/10.1109/ACCESS.2024.3386826
Raj J, J. S., & Gopalan, A. (2025). Compact Bi-slot Patch Antenna with Tapered Edges for Ka-Band Applications Featuring Machine Learning-Assisted Performance Prediction. International Journal of Environment, Engineering and Education, 7(3), 196–207. https://doi.org/10.55151/ijeedu.v7i3.326
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788. https://doi.org/10.1109/CVPR.2016.91
Rosberg, F., Aksoy, E. E., Englund, C., & Alonso-Fernandez, F. (2023). FIVA: Facial Image and Video Anonymization and Anonymization Defense. 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 362–371. https://doi.org/10.1109/ICCVW60793.2023.00043
Sapkota, R., Qureshi, R., Flores-Calero, M., Badgujar, C., Nepal, U., Poulose, A., Zenorobotics, P. Z., Billings, L., Bhanu, U., Vaddevolu, P., Khan, S., Shoman, M., Yan, H., & Karkee, M. (n.d.). YOLOv12 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series. Retrieved doi: 10.48550/arXiv.2406.19407
Sawarkar, C. D., & Sawarkar, G. B. (2024). Face identification and blurring the face using deep learning based approaches in videos. International Journal for Multidisciplinary Research (IJFMR). https://www.ijfmr.com/research-paper.php?id=14479
Shepley, A. J., Falzon, G., Kwan, P., & Brankovic, L. (2023). Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 11561–11574. https://doi.org/10.1109/TPAMI.2023.3273210
Shifa, A., Asghar, M. N., Fleury, M., Kanwal, N., Ansari, M. S., Lee, B., Herbst, M., & Qiao, Y. (2020). MuLViS: Multi-level encryption based security system for surveillance videos. IEEE Access, 8, 177131–177155. https://doi.org/10.1109/ACCESS.2020.3024926
Shifa, A., Imtiaz, M. B., Asghar, M. N., & Fleury, M. (2020). Skin detection and lightweight encryption for privacy protection in real-time surveillance applications. Image and Vision Computing, 94. https://doi.org/10.1016/j.imavis.2019.103859
Song, G., Leng, B., Liu, Y., Hetang, C., & Cai, S. (2017). Region-based Quality Estimation Network for Large-scale Person Re-identification. doi:10.1609/aaai.v32i1.12305
Sukkar, M., Kumar, D., & Sindha, J. (2021). Real-Time Pedestrians Detection by YOLOv5. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 01–06. https://doi.org/10.1109/ICCCNT51525.2021.9579808
Tran, B., Reddy Kona, S. H., Liang, X., Ghinita, G., Summerour, C., & Batsis, J. A. (2023). VPASS: Voice Privacy Assistant System for Monitoring In-home Voice Commands. 2023 20th Annual International Conference on Privacy, Security and Trust (PST), 1–10. https://doi.org/10.1109/PST58708.2023.10320179
Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., & Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. doi.org/10.48550/arXiv.2405.14458%0A
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2023). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7464–7475. https://doi.org/10.1109/CVPR52729.2023.00721
Wang, C.-Y., Yeh, I.-H., & Liao, H.-Y. M. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. https://doi.org/10.48550/arXiv.2402.13616
Wang, G., Lai, J., Huang, P., & Xie, X. (2018). Spatial-Temporal Person Re-identification. https://doi.org/10.48550/arXiv.1812.03282
Wang, T., Gong, S., Zhu, X., & Wang, S. (2016). Person Re-Identification by Discriminative Selection in Video Ranking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(12), 2501–2514. https://doi.org/10.1109/TPAMI.2016.2522418
Wei, L., Zhang, S., Gao, W., & Tian, Q. (2017). Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. https://doi.org/10.48550/arXiv.1711.08565
Wei, Q., Hu, X., Wang, X., & Wang, H. (2022). Improved RetinaNet Target Detection Model. 2022 2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), 470–476. https://doi.org/10.1109/AHPCAI57455.2022.10087635
Wu, T.-H., Wang, T.-W., & Liu, Y.-Q. (2021). Real-Time Vehicle and Distance Detection Based on Improved Yolo v5 Network. 2021 3rd World Symposium on Artificial Intelligence (WSAI), 24–28. https://doi.org/10.1109/WSAI51899.2021.9486316
Wu, Y., Lin, Y., Dong, X., Yan, Y., Ouyang, W., & Yang, Y. (2018). Exploit the Unknown Gradually: One-Shot Video-Based Person Re-identification by Stepwise Learning. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5177–5186. https://doi.org/10.1109/CVPR.2018.00543
Xiao, T., Li, S., Wang, B., Lin, L., & Wang, X. (2017). Joint Detection and Identification Feature Learning for Person Search. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3376–3385. https://doi.org/10.1109/CVPR.2017.360
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017). Aggregated Residual Transformations for Deep Neural Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5987–5995. https://doi.org/10.1109/CVPR.2017.634
Xu, D., & Wu, Y. (2020). Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection. Sensors, 20(15), 4276. https://doi.org/10.3390/s20154276
Yu, J., & Zhang, W. (2021). Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4. Sensors, 21(9), 3263. https://doi.org/10.3390/s21093263
Zhao, Y., Gao, D., Yao, Y., Zhang, Z., Mao, B., & Yao, X. (2023). Robust Deep Learning Models against Semantic-Preserving Adversarial Attack. 2023 International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN54540.2023.10191198
Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., & Tian, Q. (2016). Person Re-identification in the Wild. https://doi.org/10.48550/arXiv.1604.02531
Zhong, Z., Sun, L., & Huo, Q. (2019). An anchor-free region proposal network for Faster R-CNN-based text detection approaches. International Journal on Document Analysis and Recognition (IJDAR), 22(3), 315–327. https://doi.org/10.1007/s10032-019-00335-y
Zhou, J., & Pun, C.-M. (2021). Personal Privacy Protection via Irrelevant Faces Tracking and Pixelation in Video Live Streaming. IEEE Transactions on Information Forensics and Security, 16, 1088–1103. https://doi.org/10.1109/TIFS.2020.3029913
Zhu, S., Zhang, C., & Zhang, X. (2017). Automating Visual Privacy Protection Using a Smart LED. Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking, 329–342. https://doi.org/10.1145/3117811.3117820
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